What topic modeling could reveal about the evolution of economics
Angela Ambrosino, Mario Aldo Cedrini, John B. Davis, Stefano Fiori, Marco Guerzoni, Massimiliano Nuccio
University of Turin Marquette University University of Amsterdam Bocconi University
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摘要与影响
The paper presents the topic modeling technique known as Latent Dirichlet Allocation (LDA), a form of text-mining aiming at discovering the hidden (latent) thematic structure in large archives of documents. By applying LDA to the full text of the economics articles stored in the JSTOR database, we show how to construct a map of the discipline over time, and illustrate the potentialities of the technique for the study of the shifting structure of economics in a time of (possible) fragmentation.
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社会科学Computational and Text Analysis Methods
scientometrics and bibliometrics research · Complex Systems and Time Series Analysis
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